Recently, neural architecture search (NAS) has successfully discovered superior models that outperform human-designed models in various tasks [1]}, [2]}. Many studies have used NAS to discover superior GANs that surpass hand-crafted ones, but there are still some under-explored problems. Early NAS-GAN works only search generator (G) with a fixed architecture of discriminator (D) to reduce search difficulty, but this limits the generator performance since GANs highly depend on the constant competition between G and D. Some recent works have explored to search both G and D to improve GAN performance, while they suffer from the instability of GAN training. AdversarialNAS [3]} is the first gradient descent (GD)-based NAS-GAN work that proposes an adversarial loss function to search G and D simultaneously in a differentiable manner, but the architectures of G and D are deeply coupled, which increases combinatorial complexity and the instability of GAN training. The ablation experiment of a subsequent GD-based NAS-GAN work [4]} shows that simultaneously searching both G and D hampers the search of optimal GANs. DGGAN alleviates instability by progressively growing G and D in an alternating way but takes 580 GPU days to search on the CIFAR-10 dataset [5]}.
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